The document discusses 13 emerging technology trends for the next few years: 1) Artificial Intelligence and Machine Learning, 2) Intelligent Apps, 3) Intelligent Things, 4) Virtual and Augmented Reality, 5) Digital Twins, 6) Blockchain, 7) Conversational Systems, 8) Digital Technology Platforms, 9) Adaptive Security Architecture, 10) Mesh App & Service Architecture, 11) Humanized Big Data, 12) Physical Digital Integrations, and 13) Everything on Demand. Each trend is briefly described and examples are provided. The document cautions that accurately predicting technology's future is difficult due to potential surprises.

1. @BetaGroup /betagroup
In order to complete Thierry Geert’s Google New Trends presentation, here are some extra trends that might interest you…

2. Summary
-1) Artificial Intelligence and Machine Learning
-2) Intelligent Apps
-3) Intelligent Things
-4) Virtual and Augmented Reality
-5) Digital Twins
-6) Blockchain
-7) Converssational Systems (Dialog System)
-8) Digital Technology Platforms
-9) Adapative Security Architecture
-10) Mesh App & Service Architecture
-11) Humanized Big Data
-12) Physical Digital Integrations
-13) Everything on Demand

3. 1) ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
Artificial intelligence (AI) and advanced machine learning
(ML) are made up of technologies and processes like
deep learning and neural networks. What began as
algorithms to automate manual tasks, borrowing from
advanced statistical techniques has developed into a
broader framework and architecture that learns like a
human might, and can use historical data to predict the
future. These systems will become more adaptable and
potentially operate autonomously.
In banking, you could use AI and machine-learning techniques to model
current real-time transactions, as well as predictive models of
transactions based on their likelihood of being fraudulent.
Organizations seeking to drive digital innovation with this trend should
evaluate a number of business scenarios in which AI and machine
learning could drive clear and specific business value and consider
experimenting with one or two high-impact scenarios..

4. There are also a lot of startups who deals with Machine Learning:

5. Below are 5 examples of machine learning that really ground what machine learning is all about.
-Spam Detection: Given email in an inbox, identify those email messages that are spam and those
that are not. Having a model of this problem would allow a program to leave non-spam emails in the
inbox and move spam emails to a spam folder. We should all be familiar with this example.
-Credit Card Fraud Detection: Given credit card transactions for a customer in a month, identify those
transactions that were made by the customer and those that were not. A program with a model of
this decision could refund those transactions that were fraudulent.
-Digit Recognition: Given a zip codes hand written on envelops, identify the digit for each hand
written character. A model of this problem would allow a computer program to read and understand
handwritten zip codes and sort envelops by geographic region.
-Speech Understanding: Given an utterance from a user, identify the specific request made by the
user. A model of this problem would allow a program to understand and make an attempt to fulfil
that request. The iPhone with Siri has this capability.
-Face Detection: Given a digital photo album of many hundreds of digital photographs, identify those
photos that include a given person. A model of this decision process would allow a program to
organize photos by person. Some cameras and software like iPhoto has this capability.

6. 2) INTELLIGENT APPS
Intelligent apps, which include technologies like virtual
personal assistants (VPAs), have the potential to
transform the workplace by making everyday tasks easier
(prioritizing emails) and its users more effective
(highlighting important content and interactions).
However, intelligent apps are not limited to new digital
assistants – every existing software category from
security tooling to enterprise applications such as
marketing or enterprise resource planning (ERP) will be
infused with AI enabled capabilities.
Using AI, technology providers will focus on three areas —
advanced analytics, AI-powered and increasingly autonomous
business processes and AI-powered immersive, conversational
and continuous interfaces. By 2018, Gartner expects most of
the world’s largest 200 companies to exploit intelligent apps
and utilize the full toolkit of big data and analytics tools to
refine their offers and improve customer experience.

7. 3) INTELLIGENT THINGS
For good reason, much has been written about the
power of the Internet of Things. Intelligent things will
leverage AI and ML to interact with humans and
surroundings. Prominent examples are self-driving cars,
drones, the artifacts that will increasingly make up the
smart kitchen and smart home. Gartner predicts that
these will increasingly be woven together into a fabric
that will enhance our lives.
As intelligent things evolve and become more popular,
they will shift from a stand-alone to a collaborative
model in which intelligent things communicate with one
another and act in concert to accomplish tasks. However,
nontechnical issues such as liability and privacy, along
with the complexity of creating highly specialized
assistants, will slow embedded intelligence in some
scenarios.

8. 4) VIRTUAL & AUGMENTED REALITY
Virtual reality (VR) and augmented reality (AR)
transform the way individuals interact with each other
and with software systems creating an immersive
environment. For example, VR can be used for
training scenarios and remote experiences. AR, which
enables a blending of the real and virtual worlds,
means businesses can overlay graphics onto real-
world objects, such as hidden wires on the image of a
wall. Immersive experiences with AR and VR are
reaching tipping points in terms of price and capability
but will not replace other interface models. Over time
AR and VR expand beyond visual immersion to include
all human senses. Enterprises should look for
targeted applications of VR and AR through 2020.
The differences between virtual and augmented reality is not often well defined. True virtual
reality completely blocks out the real world whereas augmented reality adds to the already
existing real world. Sometimes these forms that are somewhere between virtual and augmented
reality are defined by other terms. For example, mixed reality is a mix of a digitized model of the
real world combined with computer-generated models.

9. For example AR can be showed on a head-mounted
display (HMD) which is a display device paired to the
forehead such as a harness or helmet. HMDs place
images of both the physical world and virtual objects
over the user's field of view. Modern HMDs often employ
sensors for six degrees of freedom monitoring that allow
the system to align virtual information to the physical
world and adjust accordingly with the user's head
movements. HMDs can provide VR users mobile and
collaborative experiences. Specific providers, such as
uSens and Gestigon, are even including gesture controls
for full virtual immersion.
Other displays are: -Eyeglasses, -HUD, -Contact Lenses, -Virtual retinal display, -Eye Tap, -
Handheld, -Spatial, -Tracking, -Input devices.

10. 5) DIGITAL TWINS
Within three to five years, billions of things will be represented by digital twins, a dynamic software
model of a physical thing or system. Using physics data on how the components of a thing operate
and respond to the environment as well as data provided by sensors in the physical world, a digital
twin can be used to analyze and simulate real world conditions, responds to changes, improve
operations and add value. Digital twins function as proxies for the combination of skilled individuals
(e.g., technicians) and traditional monitoring devices and controls (e.g., pressure gauges).
Their proliferation will require a
cultural change, as those who
understand the maintenance of real-
world things collaborate with data
scientists and IT professionals.
Digital twins of physical assets
combined with digital representations
of facilities and environments as well
detailed digital representation of the real world for simulation, analysis and control.
as people, businesses and processes will enable an increasingly

11. One example of digital twins can be the use of 3D modeling to create a digital companion for
the physical object. It can be used to view the status of the actual physical object, which
provides a way to project physical objects into the digital world. For example, when sensors
collect data from a connected device, the sensor data can be used to update a "digital twin"
copy of the device's state in real time. The term "device shadow" is also used for the concept
of a digital twin. The digital twin is meant to be an up-to-date and accurate copy of the
physical object's properties and states, including shape, position, gesture, status and motion.
In another context, Digital twin can be also used for
monitoring, diagnostics and prognostics. In this field,
sensory data is sufficient for building digital twins. These
models help to improve the outcome of prognostics by
using and archiving historical information of physical
assets and perform comparison between fleet of
geographically distributed machines. Therefore, complex
prognostics and Intelligent Maintenance System
platforms can leverage the use of digital twins in finding
the root cause of issues and improve productivity.

12. 6) BLOCKCHAIN
Blockchain is a type of distributed ledger in which value
exchange transactions (in bitcoin or other token) are
sequentially grouped into blocks. Blockchain and
distributed-ledger concepts are gaining traction because
they hold the promise of transforming industry operating
models in industries such as music distribution, identify
verification and title registry.
They promise a model to add trust to untrusted
environments and reduce business friction by providing
transparent access to the information in the chain.
While there is a great deal of interest the majority of blockchain initiatives are in alpha or beta
phases and significant technology challenges exist.
Based on the Bitcoin protocol, the blockchain database is shared by all nodes participating in a
system. The full copy of the blockchain has records of every Bitcoin transaction ever executed.
It can thus provide insight about facts like how much value belonged a particular address at any
point in the past.

13. Blockchain is secured way of online transaction. It typically
follows following workflow:
Step 1: Digitally signed transaction initiation
Step 2: Transaction is sent to miner. Miner is technically verifier
of all transactions
Step 3: Transaction is broadcast to all connected nodes as block
Step 4: Network accepts transaction if data is valid
Step 5: Receiver receives the transaction

14. 7) CONVERSATIONAL SYSTEMS (DIALOG SYSTEM)
Conversational systems can range from simple informal, bidirectional text or voice conversations
such as an answer to “What time is it?” to more complex interactions such as collecting oral
testimony from crime witnesses to generate a sketch of a suspect. Conversational systems shift
from a model where people adapt to computers to one where the computer “hears” and adapts to
a person’s desired outcome. Conversational systems do not use text/voice as the exclusive interface
but enable people and machines to use multiple modalities (e.g., sight, sound, tactile, etc.) to
communicate across the digital device mesh (e.g., sensors, appliances, IoT systems).
There are many different architectures for dialog
systems. What sets of components are included in a
dialog system, and how those components divide up
responsibilities differs from system to system. Principal
to any dialog system is the dialog manager, which is a
component that manages the state of the dialog, and
dialog strategy.

15. 1)The user speaks, and the input is converted to
plain text by the system's input
recognizer/decoder, which may include:
-automatic speech recognizer (ASR)
-gesture recognizer
-handwriting recognizer
2)The text is analyzed by a Natural language
understanding unit (NLU), which may include:
-Proper Name identification
-part of speech tagging
-Syntactic/semantic parser
3)The semantic information is analyzed by the
dialog manager, that keeps the history and state
of the dialog and manages the general flow of the
conversation.
4)Usually, the dialog manager contacts one or
more task managers, that have knowledge of
the specific task domain.
5)The dialog manager produces output using
an output generator, which may include:
-natural language generator
-gesture generator
-layout engine
6)Finally, the output is rendered using an
output renderer, which may include:
-text-to-speech engine (TTS)
-talking head
-robot or avatar
Dialog systems that are based on a text-only
interface (e.g. text-based chat) contain only
stages 2–5.
A typical activity cycle in a dialog system contains the following phases:

16. 8) DIGITAL TECHNOLOGY PLATFORMS
Digital technology platforms are the building blocks for a digital business and are necessary to break
into digital. Every organization will have some mix of five digital technology platforms: Information
systems, customer experience, analytics and intelligence, the Internet of Things and business
ecosystems. In particular new platforms and services for IoT, AI and conversational systems will be a
key focus through 2020. Companies should identify how industry platforms will evolve and plan
ways to evolve their platforms to meet the challenges of digital business.
There are five major focal points to enable digital
capabilities and business models:
-Information systems
-Customer experience
-Analytics and intelligence
-IoT
-Business ecosystems
Organizations will increasingly have a mix from across
these five digital technology platforms.

17. 9) ADAPTIVE SECURITY ARCHITECTURE
The evolution of the intelligent digital mesh and digital technology platforms and application architectures
means that security has to become fluid and adaptive. Security in the IoT environment is particularly
challenging. Security teams need to work with application, solution and enterprise architects to consider
security early in the design of applications or IoT solutions. Multilayered security and use of user and entity
behavior analytics will become a requirement for virtually every enterprise.
Sun Microsoft lists the following as the objectives of Adaptive Security
Architecture:
-Reduce threat amplification – it restricts the potential spread of a
pandemic in a monoculture.
-Shrink the attack surface – make the target of an attack smaller
-Decrease attack velocity – slow the rate of attack
-Reduce remediation time – respond to an attack quickly
-Facilitate the availability of data and processing resources – prevent or contain attacks that try to limit
resources
-Promote correctness of data and the reliability of processing resources – respond to attacks intended to
compromise data or system integrity.

18. 10) MESH APP & SERVICE ARCHITECTURE
In the mesh app and service architecture (MASA), mobile
apps, web apps, desktop apps and IoT apps link to a
broad mesh of backend services to create what users
view as an "application." The architecture encapsulates
services and exposes APIs at multiple levels and across
organizational boundaries, balancing the demand for
agility and scalability of services with composition and
reuse of services. The MASA enables users to have an
optimized solution for targeted endpoints in the digital
mesh (e.g., desktop, smartphone, automobiles) as well
as a continuous experience as they shift across these
different channels.

19. consumers can invoke services by sending messages. These messages are typically
transformed and routed by a service bus to an appropriate service implementation. This
service architecture can provide a business rules engine that allows business rules to be
incorporated in a service or across services. The service architecture also provides a
service management infrastructure that manages services and activities like auditing,
billing, and logging. In addition, the architecture offers enterprises the flexibility of
having agile business processes, better addresses the regulatory requirements like
Sarbanes Oxley (SOX), and changes individual services without affecting other services.
An example of service architecture is :

20. 11) HUMANIZED BIG DATA
Big data has been a big topic for the past five
years or so, when it started making headlines
as a buzzword. The idea is that mass quantities
of gathered data—which we now have access
to—can help us in everything from planning
better medical treatments to executing better
marketing campaigns. But big data’s greatest
strength—its quantitative, numerical
foundation—is also a weakness.
In 2017 we’ll see advancements to humanize big data, seeking more empathetic and
qualitative bits of data and projecting it in a more visualized, accessible way.

21. 12) PHYSICAL DIGITAL INTEGRATIONS
Mobile devices have been slowly adding technology into
our daily lives. It’s rare to see anyone without a
smartphone at any given time, giving us access to
practically infinite information in the real-world. We
already have things like site-to-store purchasing,
enabling online customers to buy and pick up products in
a physical retail location, but the next level will be even
further integrations between physical and digital
realities. Online brands like Amazon will start having
more physical products, like Dash Buttons, and physical
brands like Walmart will start having more digital
features, like store maps and product trials.

22. 13) EVERYTHING ON DEMAND
Thanks to brands like Uber (and the resulting madness of
startups built on the premise of being the “Uber of ____”),
people are getting used to having everything on demand via
phone apps. In 2017, we all expect to see this develop even
further. We have thousands of apps available to us to get
rides, food deliveries, and even a place to stay for the night,
but soon we’ll see this evolve into even stranger territory.
Anyone in the tech industry knows that making predictions
about the course of technology’s future, even a year out, is
an exercise in futility.
Surprises can come from a number of different directions, and announced
developments rarely release as they’re intended.
Still, it pays to forecast what’s coming next so you can prepare your marketing
strategies (or your budget) accordingly.

23. Another example is Just Eat which is an online food order and delivery service. It acts as an
intermediary between independent take-out food outlets and customers. It is
headquartered in the United Kingdom and operates in 13 countries in Europe, Asia,
Oceania, and the Americas. The platform allows customers to search for local take-out
restaurants to place orders online, and to choose from pick-up or delivery options.

24. We hope that this document was interesting for you. As it was
mentioned above:
Anyone in the tech industry knows that making predictions
about the course of technology’s future, even a year out, is an
exercise in futility.
Surprises can come from a number of different directions, and
announced developments rarely release as they’re intended.
Still, it pays to forecast what’s coming next so you can prepare
your marketing strategies (or your budget) accordingly.
BetaGroup Team
Sources: Forbes, Gartner, Wikipedia & innovationexcellence.com
@BetaGroup /betagroup